Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate

Josephine Lukito, Prathusha K Sarma, Jordan Foley, Aman Abhishek


Abstract
This paper proposes a method for identifying and studying viral moments or highlights during a political debate. Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments during the 2016 U.S. Presidential election. First, a time series outlier analysis is used to identify key moments during the debate. These moments had to result in a long-term shift in attention towards either Hillary Clinton or Donald Trump (i.e., a transient change outlier or an intervention, resulting in a permanent change in the time series). To assess whether these moments also resulted in a discursive shift, two corpora are produced for each potential viral moment (a pre-viral corpus and post-viral corpus). A domain adaptation layer learns weights to combine a generic and domain-specific (DS) word embedding into a domain adapted (DA) embedding. Words are then classified using a generic encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.
Anthology ID:
W19-2107
Volume:
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | NLP+CSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–64
Language:
URL:
https://aclanthology.org/W19-2107
DOI:
10.18653/v1/W19-2107
Bibkey:
Cite (ACL):
Josephine Lukito, Prathusha K Sarma, Jordan Foley, and Aman Abhishek. 2019. Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate. In Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, pages 54–64, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate (Lukito et al., 2019)
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PDF:
https://preview.aclanthology.org/update-css-js/W19-2107.pdf